Assistant Professor
638A Boyd GSRC 706-542-4661

We study and develop in silico methods for quantifying spatiotemporal phenomena in the context of improving public health. This takes several forms: high-throughput image analysis of GFP-tagged z-stacks, machine learning to detect ciliary motion abnormalities in high-speed videomicroscope data, or applied statistics to predict and identify disease outbreaks.